AttriClaim

Insurance decisions increasingly combine source data, automated models, deterministic rules, workflow systems and human judgment. When a claim or underwriting outcome is questioned, teams need to reconstruct what happened without pretending those contributions can be divided into precise percentages. The supplied research confirms adjacent decision-accountability and model-observability products, but not the proposed percentage split.

AttriClaim should therefore reject the original AI-versus-human apportionment premise. It would preserve a chronological event ledger: source versions, model request and output, model version, deterministic rule, policy version, presented recommendation, human review, correction, override, approval, downstream action and readback. Each record would state its source, timestamp, actor class, integrity evidence and telemetry gaps.

A signature or hash can support bounded integrity and provenance. It cannot prove that input data were true, that a model caused an outcome, that a decision was fair, that a reviewer exercised meaningful judgment or that liability belongs to one party. Counts grouped by documented automation class may support governance, but an autonomy ratio would be misleading unless the organization first defines and validates every class and denominator.

Potential rubber-stamping patterns are review candidates, not findings about employees. The product must not score workers or automate discipline. Insurance fairness, discrimination, adverse-action duties, consumer notices, coverage interpretations and legal responsibility require qualified external review. The buyer hypothesis is a carrier or administrator governance, claims or underwriting-technology leader; budget, telemetry access, workflow authority and current alternative need validation.

A carrier or third-party administrator governance, claims, underwriting-technology or risk leader responsible for reconstructing high-impact decision processes.